A comprehensive characterization of MPS-STR stutter artefacts. (September 2022)

• Record Type: Journal Article
• Authors: Agudo, Maria Martin; Aanes, Håvard; Roseth, Arne; Albert, Michel; Gill, Peter; Bleka, Øyvind
• Is Part Of: Forensic science international. Volume 60(2022)
• Publication Date: 2022-09
• Subjects: Stutter -- Proportions -- MPS -- Beta regression -- PTUS -- LUS; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2022.102728
• Languages: English
• ISSNs: 1872-4973

Abstract

Abstract: Interpretation of DNA evidence involving mixtures is challenging when alleles from minor contributors coincide with stutters from major contributors. To accommodate this, it is important to have a good understanding of stutter sequence formation trends. Here, multiple stutter types were characterized based on massively parallel sequencing (MPS) data from 387 single source samples, using the Verogen ForenSeq™ DNA Signature Prep kit. A beta regression model was used to investigate the relationship between the stutter proportion and candidate explanatory variables. In the final model, stutter proportions were explained by the length of the parental uninterrupted stretch (PTUS), which is comparable to block length of the missing motif (BLMM). Also, different stutter types (n+1, n-1, n+2, n-2, n0) were analyzed separately per locus. The fitted stutter models were then integrated into an extended probabilistic genotyping model based on EuroForMix (MPSproto). An illustrative minor/major mock mixture example is discussed. Evaluation of multiple types of stutters on a per locus basis improved the probabilistic genotyping result compared to the conventional EuroForMix model, using the LUS+ nomenclature. Highlights: Stutter artefacts from STRs in MPS forensic data are characterized. Stutter proportions and candidate variables are investigated via beta regression. Parental uninterrupted stretch (PTUS) as explanatory variable. Estimates are integrated into probabilistic genotyping model MPSproto.